Optimal Operating Depth Search for Active Towed Array Sonar using Simulated Annealing

In an active towed array sonar, it is important to find the optimal operation depth. Generally, the optimal depth can be chosen via numerical simulations for all sonar depths and this imposes great burdens of time and cost.In this paper, an efficient approach is proposed to find the optimal depth using the optimisation technique. First, the sonar performance function is newly defined as a measure of how well the active sonar might perform. This function depends on the properties of the ocean environment and the positions of sonar and underwater target. Then, the simulated annealing to find an optimal solution for maximising sonar performance is used. The optimised depth agrees well with the depth obtained from direct searching for all depths of source and receiver combinations, but its computational time is largely reduced

Three-Dimensional Passive Source Localisation using the Flank Array of an Autonomous Underwater Vehicle in Shallow Water

Researchers have become interested in autonomous underwater vehicles equipped with various kinds of sonar systems that can perform many of underwater tasks, which is encouraged by the potential benefits of cost reduction and flexible deployment. This paper proposes an approach to three-dimensional passive source localisation with the flank array of an autonomous underwater vehicle in shallow water. The approach is developed based on matched-field processing for the likelihood of passive source localisation in the shallow water environment. Inter-position processing is also used for the improved localisation performance and the enhanced stability of the estimation process against the lack of spatial gain due to the small physical size of the flank array. The proposed approach is presented and validated through simulation and experimental data. The results illustrate the localisation performance at different signal-to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the autonomous underwater vehicle cruises at a low speed along a straight line at a constant depth.Defence Science Journal, 2013, 63(3), pp.323-330, DOI:http://dx.doi.org/10.14429/dsj.63.301

Observation of the Relationship between Ocean Bathymetry and Acoustic Bearing-Time Record Patterns Acquired during a Reverberation Experiment in the Southwestern Continental Margin of the Ulleung Basin, Korea

We observed a distinct drop-off region in the bearing-time record of acoustic reverberation data acquired from the south-western continental margin of the Ulleung Basin, East Sea, in the summer of 2015. 3 kHz continuous waves with pulse lengths of 0.1, 0.3, and 1.0 s were used as source pulses, with an R/V Cheonghae vessel towing a variable depth source and a triplet towed array toward the deep sea from shallow water. The observed pattern changed as the R/V Cheonghae moved across the continental slope further into the sea. This pattern arises as a result of the downward-refracted beams in the 1/2 convergence zone interacting with the soft bottom. In addition, the boundary of the drop-off region was modeled with the two-way maximum travel time of the first bottom-reflected rays using the bathymetry model of the General Bathymetric Chart of the Oceans, 2020. Some discrepancies were observed when comparing the modeled curve to the measured results, and the inaccuracy of the bathymetry model on the continental slope could be the main cause of these discrepancies. This pattern could be useful for bathymetry mapping, as well as estimations of source and receiver configurations

A Modified Recursive Regularization Factor Calculation for Sparse RLS Algorithm with l1-Norm

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity

Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning

Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results

Detection and hazard assessment of pathogenic microorganisms in medical wastes

This study was undertaken to investigate the types and concentrations of microbial agents in various medical wastes as well as to characterize their survivals in these wastes at different temperatures for microbial risk assessment. Medical wastes collected from 5 major hospitals in South Korea were classified and stored at three different temperatures (-20, 6, and 30 degrees C). Presence of various microorganisms such as pathogenic viruses and bacteria were investigated by both cultivation and by (RT)-PCR assays. A number of (opportunistic) pathogenic bacteria, including Pseudomonas spp., Lactobacillus spp., Staphylococcus spp., Micrococcus spp., Kocuria spp., Brevibacillus spp., Microbacterium oxydans, and Propionibacterium acnes, were identified from the various medical wastes. In addition, pathogenic viruses such as noroviruses and hepatitis B virus were also detected in one of the human tissue wastes. Commonly identified bacterial and viral pathogens such as Pseudomonas spp., Corynebacterium diphtheriae, Escherichia coli, Staphylococcus spp., and respiratory synctial virus (RSV) were inoculated into either gauzes or diapers, and their survivals were characterized. Viral agents such as RSV showed poor survival in most environmental conditions, and demonstrated that various pathogens could be present in medical wastes but that the associated health risk appeared to be low. However, medical waste should be carefully controlled and monitored to prevent nosocomial infection associated with the exposure to these wastes.Lee J, 2008, APPL ENVIRON MICROB, V74, P2111, DOI 10.1128/AEM.02442-07Marinkovic N, 2008, WASTE MANAGE, V28, P1049, DOI 10.1016/j.wasman.2007.01.021Shariati B, 2007, J OCCUP HEALTH, V49, P317de Bruin E, 2006, J VIROL METHODS, V137, P259, DOI 10.1016/j.jviromet.2006.06.024Garcia C, 2006, J CLIN MICROBIOL, V44, P2997, DOI 10.1128/JCM.00065-06Jang YC, 2006, J ENVIRON MANAGE, V80, P107, DOI 10.1016/j.jenvman.2005.08.018ZIEBUHR W, 2006, INT J ANTIMICROB AG, V28, P14Phipps LP, 2004, J VIROL METHODS, V122, P119, DOI 10.1016/j/jviromet.2004.08.008Loberto JCS, 2004, BRAZ J MICROBIOL, V35, P64Seo SH, 2002, NAT MED, V8, P950, DOI 10.1038/nm757Nema SK, 2002, CURR SCI INDIA, V83, P271Katayama K, 2002, VIROLOGY, V299, P225, DOI 10.1006/viro.2002.1568Katayama H, 2002, APPL ENVIRON MICROB, V68, P1033, DOI 10.1128/AEM.68.3.1033-1039.2002Monpoeho S, 2001, APPL ENVIRON MICROB, V67, P2484SALKIN IF, 2001, REV HLTH IMPACTS MICManfredi R, 2000, EUR J EPIDEMIOL, V16, P111Kuo HW, 1999, WATER AIR SOIL POLL, V114, P413Kim BJ, 1999, J CLIN MICROBIOL, V37, P1714HAAS C, 1999, QUANTITATIVE MICROBIKane A, 1999, B WORLD HEALTH ORGAN, V77, P801PRUSS A, 1999, SAFE MANAGEMENT WAST, P20Schlegel L, 1998, EUR J CLIN MICROBIOL, V17, P887Frank U, 1997, CLIN INFECT DIS, V25, P318BELL DM, 1997, AM J MED S5B, V102, P9Lee CC, 1996, J HAZARD MATER, V48, P1GOLDENBERGER D, 1995, PCR METH APPL, V4, P368OHMAN SC, 1995, ACTA ODONTOL SCAND, V53, P49PATEL R, 1994, CLIN INFECT DIS, V18, P207PATTI JM, 1994, INFECT IMMUN, V62, P152TANAKA MM, 1994RUTALA WA, 1992, INFECT CONT HOSP EP, V13, P3848EDWARDS U, 1989, NUCLEIC ACIDS RES, V17, P7843